Scheduling pervasive IoT devices and designing a collaboration strategy to conduct online inference tasks while being constrained by computation and memory resources in addition to privacy issues, is a complex resource allocation problem. Recently, a lot of research works have used the RL for a variety of applications and systems, spanning from wireless networks [1]}, [2]} and mobile edge computing [3]} to ride-sourcing services [4]} and crowdsourcing systems [5]}, owing to the ability of reinforcement learning to efficiently solve real-time and dynamic problems. The power of the RL technique is that it is independent from any static knowledge about the system and it relies on the try-and-error interaction with the environment to learn the most effective actions and adapt to any system fluctuation. On these basis, reinforcement learning will be used as a tool to solve our resource allocation environment.
